Nana Kena Frempong

Statistics and Actuarial Science

About

Nana Kena Frempong is an Associate Professor in the Department of Statistics and Actuarial Science at the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. He received a Ph.D. in Mathematical Statistics from the Kwame Nkrumah University of Science and Technology in October 2017. He was awarded two Master’s degrees in Applied Statistics and Biostatistics respectively, at the Universiteit Hasselt, Belgium, in 2007. His primary research interest is in the theory of estimation which focuses on deriving methodologies that help to improve statistical learning. He is interested in identifying new ways to apply methods from different disciplines in new settings. This includes modifying methodology from one area of statistics so that it may be applied in a separate area, or through applying statistical methods to novel problems in the real world of data analysis. Currently, he is involved with some data science techniques in Machine Learning. Nana Kena Frempong is passionate about the value of statistical thinking, and the use of statistical techniques as a means for improving decision-making. He collaborates with other scientists from engineering, biochemistry, building and construction. His passion is to make students understand the subject matter. He has a number of publications in referred journals and has published more than forty (40) articles in the Mathematics, Finance, Economics, and Statistics literature.

Research Summary

(inferred from publications by AI)

The researcher's work is centered on integrating mathematical models with empirical data across diverse scientific domains, particularly in areas where predictive analytics and statistical methods are applied to solve complex problems. Foundational contributions include early explorations in fractional differential equations, which have since been expanded into broader applications. The researcher has integrated methodologies such as machine learning, statistical distributions, predictive maintenance models, Bayesian approaches, and quantum computing architectures into their work. This interdisciplinary approach spans fields like energy, finance, health sciences, education, and social sciences, demonstrating a trend towards applying theoretical advancements to real-world challenges. Their research highlights contributions through various methodologies and the development of practical tools for electoral modeling, financial copulas, stock market forecasting, biometric security, pharmacological studies, psychometric methods, and even medical imaging. This approach underscores both theoretical innovations and applied solutions, reflecting a holistic exploration of mathematical and statistical applications in scientific endeavors.

Research Themes

All Papers

Fractional Order SIR Model with Constant Population(2016)

Fractional Order Malaria Model With Temporary Immunity(2016)

Predicting likelihood of cost overrun in educational projects(2017)

Parametric time overrun estimation of building projects(2016)

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm(2021)

Does the data tell the true story? A modelling assessment of early COVID-19 pandemic suppression and mitigation strategies in Ghana(2021)

Does the Data Tell the True Story? A Modelling Study of Early COVID-19 Pandemic Suppression and Mitigation Strategies in Ghana(2021)

Ghana COVID-19 Daily Case Data by Date Sample Taken March-November 2020(2021)

Dynamic linear state space model for forecasting peak and short-term electricity demand using kalman filtered monte carlo method(2021)

Corrigendum to “Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method” [Heliyon Volume 9, Issue 8, August 2023, Article e18821](2025)

Hybrid intelligent predictive maintenance model for multiclass fault classification(2023)

Predictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification(2022)

A Mixture of Clayton, Gumbel, and Frank Copulas: A Complete Dependence Model(2022)

Hybrid Model for Stock Market Volatility(2023)

Simulating stock prices using geometric Brownian motion model under normal and convoluted distributional assumptions(2023)

Volatility Assessment of Equities on the Ghana Stock Exchange(2015)

Hybrid Clayton-Frank Convolution-Based Bivariate Archimedean Copula(2018)

On Two Random Variables and Archimedean Copulas(2017)

On a Hybrid Clayton-Gumbel and Gumbel-Frank Bivariate Copulas with Application to Stock Indices(2018)

Statistical Modeling for Prediction of CCT Diagrams of Steels Involving Interaction of Alloying Elements(2020)

Application of generalized estimating equation (GEE) model on students' academic performance(2014)

A New Generated Family of Distributions: Statistical Properties and Applications with Real-Life Data(2023)

The Odd Ramos-Louzada Generator of distributions with applications to failure and waiting times(2023)

Modified Ramos-Louzada-G family with baseline Weibull distribution: Properties, characterizations, regression, and applications(2024)

Generalization of Odd Ramos-Louzada generated family of distributions: Properties, characterizations, and applications to diabetes and cancer survival datasets(2024)

The incidence of malaria and the comparison of hematological and biochemical indices of Plasmodium falciparum‐parasitemic and aparasitemic sickle cell disease (SCD) patients(2010)

An SITR Analysis of Treatment Model of Hepatitis B Epidemic(2015)

Modelling the volatility of the Ghana stock market: A comparative study(2023)

Time Series Analysis of the Exchange Rate of the Ghanaian Cedi to the American Dollar(2015)

Modeling Stock Market Volatility Using GARCH Approach on the Ghana Stock Exchange(2015)

Threshold Analysis of Wavelet Based Fingerprint Feature Extraction Methods on Multiple Impression Dataset(2014)

Fitting Finite Mixtures of Generalized Linear Regressions on Motor Insurance Claims(2017)

A Mathematical Model for Mycolactone Toxin Reaction and Di usion in Cell Cytoplasm(2018)

Improved estimators for the rate parameter of gamma model using asymptotic properties(2021)

Item Response Theory Model for Understanding Item Non- Response in Ghanaian Surveys(2014)

An Item Response Model for Understanding Item Non-response in Ghanaian Surveys(2014)

Emerging Relationships Between Macroeconomic Indicators and Real Economic Activities in Ghanaian Economy(2015)

On Some Compartmental Models for Ebola Disease(2016)

Accessing Individual Students Academic Performance Using Random Effect Analysis (Multilevel Analysis)(2016)

The Fundamental Matrix of the Simple Random Walk with Mixed Barriers(2016)

A simulation study to examine the bias of some sample measures of skewness(2021)

A parametric model for estimating force of infection for hepatitis B-case study Komfo Anokye Teaching Hospital(2021)

Wavelets Based Feature Extraction With PCA For Predicting Autism In Neonates Using Navïe Bayes Classifier(2021)

Efficient Training of One Class Classification-SVMs(2023)

Determinants of Length of Stay of Snakebite Victims in a Hospital. Statistical Modeling Approach(2023)

Simulation Study of Estimators of the Gamma Rate Parameter Using MLE as a Baseline Estimator(2024)

Stochastic modeling for energy generation in Sub Saharan Africa (Case Study of NAWEC Power Plants-The Gambia)(2024)

Decision-to-Incision Time of an Emergency Cesarean Delivery on Prolong Labour: Application of Partograph use at Kwadaso Municipal Hospital in the Ashanti Region of Ghana(2025)

Enhancing Student Achievement in Circle Theorems: Integrating Computer Animation with the Jigsaw Cooperative Learning Model(2025)

Collaboration Network

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Research Collaboration Map

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.